Özet:

This research aims to examine the midterm and final performance, e-assessment design perception, and general learning experiences of learners whose performance is different according to the analytics used in e-assessment. In the research, the learning analytics process was carried out and the descriptive analytics method was used. This process includes the collection and analysis of metrics that can be associated with learning performance in the periods until the midterm and the final. The study group consists of 285 students enrolled in distance education programs and taking the Information and Communication Technology course. Data were collected through pre-test for each subject, student monitoring tools in MOODLE LMS, an online assessment scale, and midterm and final exams (midterm and final). Clustering analysis (k-means and hierarchical) was used to describe learning performance. Differences between academic achievement, e-assessment, and general learning experiences by clusters were analyzed by t-test. As a result, in an e-assessment design like the one in this study, it was found that students with high performance in terms of the variables considered had higher academic achievement. However, it is argued that due to the limitations in the regulations, a fair assessment process cannot be guaranteed. In this respect, it may be beneficial to focus on how to determine the success criteria better and how to increase the implementation examples that can demonstrate the learning performance in a more qualified way.

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